This section describes main characteristics of the segment data set and its attributes:

General information

Image Segmentation data set

Type

Classification

Origin

Real world

Features

19

(Real / Integer / Nominal)

(19 / 0 / 0)

Instances

2310

Classes

7

Missing values?

No

Attribute description

Attribute

Domain

Attribute

Domain

Region-centroid-col

[1.0, 254.0]

Rawred-mean

[0.0, 137.11111]

Region-centroid-row

[11.0, 251.0]

Rawblue-mean

[0.0, 150.88889]

Region-pixel-count

[9.0, 10.0]

Rawgreen-mean

[0.0, 142.55556]

Short-line-density-5

[0.0, 0.33333334]

Exred-mean

[-49.666668, 9.888889]

Short-line-density-2

[0.0, 0.22222222]

Exblue-mean

[-12.444445, 82.0]

Vedge-mean

[0.0, 29.222221]

Exgreen-mean

[-33.88889, 24.666666]

Vedge-sd

[0.0, 991.7184]

Value-mean

[0.0, 150.88889]

Hedge-mean

[0.0, 44.722225]

Saturatoin-mean

[0.0, 1.0]

Hedge-sd

[-1.5894573E-8, 1386.3292]

Hue-mean

[-3.0441751, 2.9124804]

Intensity-mean

[0.0, 143.44444]

Output

{1, 2, 3, 4, 5, 6, 7}

Additional information

This database contains instances drawn randomly from a database of 7 outdoor images (classes). The images were handsegmented to create a classification for every pixel. Each instance encodes a 3x3 region.

The task is to determine the type of surface of each region.

Attributes description:

1. Region-centroid-col: the column of the center pixel of the region.
2. Region-centroid-row: the row of the center pixel of the region.
3. Region-pixel-count: the number of pixels in a region = 9.
4. Short-line-density-5: the results of a line extractoin algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region.
5. Short-line-density-2: same as short-line-density-5 but counts lines of high contrast, greater than 5.
6. Vedge-mean: measure the contrast of horizontally adjacent pixels in the region. There are 6, and this is the mean value. This attribute is used as a vertical edge detector.
7. Vegde-sd: the contrast of horizontally adjacent pixels in the region. There are 6, and this is the standard deviation. This attribute is used as a vertical edge detector.
8. Hedge-mean: measures the contrast of vertically adjacent pixels. Used for horizontal line detection (mean)
9. Hedge-sd: measures the contrast of vertically adjacent pixels. Used for horizontal line detection (standard deviation)
10. Intensity-mean: the average over the region of (R + G + B)/3
11. Rawred-mean: the average over the region of the R value.
12. Rawblue-mean: the average over the region of the B value.
13. Rawgreen-mean: the average over the region of the G value.
14. Exred-mean: measure the excess red: (2R - (G + B))
15. Exblue-mean: measure the excess blue: (2B - (G + R))
16. Exgreen-mean: measure the excess green: (2G - (R + B))
17. Value-mean: 3-d nonlinear transformation of RGB (see James D. Foley and Andries Van Dam. 1982. Fundamentals of Interactive Computer Graphics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA)
18. Saturatoin-mean: (see James D. Foley and Andries Van Dam. 1982. Fundamentals of Interactive Computer Graphics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.)
19. Hue-mean: (see James D. Foley and Andries Van Dam. 1982. Fundamentals of Interactive Computer Graphics. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.)